code: def forward(self, x_dict, edge_index_dict):
      item_x = self.conv1(
          x_dict["item"],
          edge_index_dict[("item", "to", "item")],
      ).relu()
  
      user_x = self.conv2(
          (x_dict["item"], x_dict["user"]),
          edge_index_dict[("item", "rev_to", "user")],
      ).relu()
  
      user_x = self.conv3(
          (item_x, user_x),
          edge_index_dict[("item", "rev_to", "user")],
      ).relu()
  
      return self.lin(user_x)
  
imports: import os.path
  import torch
  import torch.nn.functional
  import tqdm
  from sklearn.metrics import roc_auc_score
  from torch.nn import Embedding, Linear
  import torch_geometric.transforms
  from torch_geometric.datasets import Taobao
  from torch_geometric.loader import LinkNeighborLoader
  from torch_geometric.nn import SAGEConv
  from torch_geometric.utils.convert import to_scipy_sparse_matrix
label_desc: Use image node_id 2 for calling the UserGNNEncoder obj's underlying member method code with example usage: obj.forward(x_dict, edge_index_dict) and returns: self